Biomedical Applications

Research projects
Related Publications
Invited lectures&talks
Student projects supervised


CI4CB research focuses on the development of new methods in computational intelligence (data analysis and  predictive modeling based on supervised and unsupervised learning) and their application to life sciences and  biomedical engineering. CI4CB special interest is to give explanatory power to AI-based models, especially based  on fuzzy logic and supervised evolutionary algorithms to build grey-box predictive models.

Research projects

Grants and external funding

  • MaLDIveS (Machine Learning DIagnostic Soil): Séquençage à ultra-haut débit, bioinformatique et Machine Learning comme outil de Diagnostic de la santé des Sols. 2017-2019. National funding: HES-SO. In collaboration with HES-SO Changins.
  • INPHINITY: In silico prediction of phage­-bacteria infection networks as a tool to implement personalized phage therapy. 2016-2019. National Funding: SNSF Project. In collaboration with the University of Lausanne and the Inselspital at Bern.
  • BOSS Explorer: Development and integration of new methods for exploration, selection, and visualization of diagnostic signatures. 2016-2017. National Funding: CTI Agency. In collaboration with SimplicityBio SA, Monthey, Switzerland.
  • FISHGUARD: Improving of fish viral diseases monitoring through the development of rapid diagnostic tests. 2015-2018. European Funding: Eurostars-Eureka Program. In collaboration with two SMEs from Poland and France.
  • HP-FuzzyBio: High-peformance Pipeline for Fuzzy Modelling in Bioinformatics. 2013-2014. National Funding: RCSO. In collaboration with Hepia (Geneva University of Applied Sciences)
  • DiagnoSuite: Suite of data analysis and data management tools for discovering, developing, deploying, and exploiting biomarker-based diagnostic screening tests. 2013-2014. National Funding: CTI Agency. In collaboration with Diagnoplex SA, Epalinges, Switzerland.
  • nanoFUGE: FUzzy modelling Gene-Expression data from Nanostring technology. 2010-2014. National Funding: SNSF Project. In collaboration with the University of Geneva and the University Hospitals of Geneva. In the frame of the SNSF Project “Leukaemia diagnosis using Nanotechnology”.
  • ISyPeM: Intelligent Integrated Systems for Personalized Medicine. 2010-2013. National Funding: Nano-Tera Initiative. Member of a large consortium of several Swiss universities.
  • TIC4AGRO: Revealing new opportunities for small-scale fruit growers in Colombia by harnessing time in a collaborative site-specific agricultural setup. 2012. National funding: KFH Agency.
  • PharMEA: Multi-Electrode Array technology based platform for industrial pharmacology and toxicology drug screening. 2009-2011. European Funding: FP7 European project, Research for SMEs instrument. In collaboration with a consortium of several European universities and SMEs.
  • High-performance reconfigurable computing toolset and development methodology for bioinformatics and computational biology applications. 2009-2010. National funding: RCSO.

Private Research Agreements

  • FlouColoX:. Systèmes flous pour le dépistage du cancer colorectal (Fuzzy systems for colorectal cancer screening). 2010-2012. Research agreement with Diagnoplex SA, Epalinges, Switzerland.
  • FUGE-LC: Fuzzy Modelling from Gene Expression Data – Lung Cancer. 2009-2011. Research agreement with Philip Morris International R&D Division.

Related Publications

Peer reviewed conference

  1. D. Leite, J. Fernando Lopez, X. Brochet, M.-A. Barreto-Sanz, Y. Que, G. Resch, and C.A. Peña, “Exploration of multiclass and one-class learning methods for prediction of phage-bacteria interaction at strain level”, Submitted for peer reviewing to the 2018 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2018).
  2. Z. Mungloo-Dilmohamud, G. Marigliano, Y. Jaufeerally-Fakim, and C.A. Peña, “A Comparative Study of Feature Selection Methods for Biomarker Discovery”, Submitted for peer reviewing to the 2018 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2018).
  3. D-V. Ramírez López, C. Peña, and Á-J. Rojas, “Agent-based modeling of mesenchymal stem cells on a 3D-printed bio-device for the regenerative treatment of the infarcted myocardium”, Submitted for peer reviewing to the 2018 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2018).
  4. D. Leite, X. Brochet, G. Resch, Y. Que, A. Neves, and C.A. Peña, Computational Prediction of Host-Pathogen Interactions Through Omics Data Analysis and Machine Learning,IWBBIO 2017Bioinformatics and Biomedical Engineering pp 360-371, 2017. DOI
  5. Z. Mungloo, Y. Jaufeerally, and C.A. Peña, A Meta-Review of Feature Selection Techniques in the Context of Microarray Data,IWBBIO 2017. Bioinformatics and Biomedical Engineering pp 33-49, 2017. DOI
  6. M.-A. Barreto-Sanz, A. Bujard and C.A. Peña, Evolving very compact fuzzy systems for gene expression data analysis. Proceedings of the IEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012, pp. 356-361, 2012. DOIPDF
  7. J. Rossier, C.A. Peña-Reyes, Extrinsic evolution of fuzzy systems applied to disease diagnosis, ICES 2010. Lecture Notes in Computer Science, Vol. 6274/2010, 226-237, 2010. DOI
  8. M.A. Melgarejo, C.A. Peña-Reyes, E. Sanchez. A genetic-fuzzy system approach to control a model of the HIV infection dynamics. Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 2323 – 2330, 2006. DOIPDF
  9. C.A. Peña-Reyes, M. Sipper, and L. Prieto. Sensitive, specific, and interpretable: Evolving a fuzzy mammographic-interpretation assessment tool. 2002 IEEE International Fuzzy Systems Conference Proceedings, pages 837-842. IEEE Neural Network Society, 2002. DOIPDF
  10. C.A. Peña-Reyes and M. Sipper. Applying Fuzzy CoCo to breast cancer diagnosis. Proceedings of the 2000 Congress on Evolutionary Computation (CEC00), volume 2, pages 1168-1175. IEEE Press, Piscataway, NJ, USA, 2000. DOIPDF
  11. C.A. Peña-Reyes and M. Sipper. Designing breast cancer diagnostic systems via a hybrid fuzzy-genetic methodology.
    IEEE International Fuzzy Systems Conference Proceedings, volume 1, pages 135-139. IEEE Neural Network Council, 1999. DOIPDF
  12. C.A. Peña-Reyes and M. Sipper. Evolving fuzzy rules for breast cancer diagnosis. Proceedings of 1998 International Symposium on Nonlinear Theory and Applications (NOLTA’98), volume 2, pages 369-372. Presses Polytechniques et Universitaires Romandes, Lausanne, 1998.

Journal Papers

  1. N. Grandjean, B. Charpiot, C.A. Peña, M. C. Peitsch.Competitive Intelligence and Patent Analysis in Drug Discovery. Mining the competitive knowledge bases and patents. Drug Discovery Today: Technologies. 3:211-215, 2005. DOI
  2. C.A. Peña-Reyes. Evolutionary fuzzy modelling human diagnostic decisions. Annals of the New York Academy of Sciences, pages 190-211, May 2004. DOI PDF
  3. C.A. Peña-Reyes and M. Sipper. Evolutionary computation in medicine: An overview. Artificial Intelligence in Medicine, 19(1):1-23, May 2000. DOI
  4. C.A. Peña-Reyes and M. Sipper. A fuzzy-genetic approach to breast cancer diagnosis. Artificial Intelligence in Medicine, 17(2):131-155, October 1999. DOI PDF

Book chapters

  • C.A. Peña-Reyes and M. Sipper. Combining evolutionary and fuzzy techniques in medical diagnosis.
    M. Schmitt, H. N. Teodorescu, A. Jain, A. Jain, S. Jain, and L. C. Jain, editors, Computational Intelligence Techniques in Medical Diagnosis and Prognosis, volume 96 of Studies in Fuzziness and Soft Computing, chapter 14, pages 391-426. Springer-Verlag, Heidelberg, 2002. DOI

Invited lectures&talks





Student projects supervised

Co-supervision of PHD Thesis

  • In Sillico prediction of phage-bacteria infection networks as a tool to implement personalized phage therapy
 (In course), Diogo Carvalho Leite, University of Lausanne, 2016 (In progress)
  • Computational methods for robust feature selection in the context of gene-expression profiling and biomarker discovery: robust and novel methods for feature selection (In course), Zahra Mungloo-Dilmohamud, University of Mauritius, 2014 (In progress)

Master Theses and Bachelor projects

  • Deep learning applied to retina images, María Camila Alvarez Triviño
, 2016, In collaboration with University “Autonoma de Occidente”, Cali, Colombia.
  • Dockerisation d’environement pour les projets de bioinformatique (Dockerization of computational tools for bioinformatics), Vincent Deruaz, HES-SO, 2016
  • Modélisation prédictive des interactions entre bactéries et virus bacteriophages (Predictive modeling of interactions between bacteria and bacteriophages), Diogo Leite, HES-SO and ISEP (Portugal), 2015-16. In collaboration with CHUV and UNIL.
  • Predictive Modelling for NMR metabolic profiles, Rui Reis, HEIG-VD, 2015. In collaboration with University del Valle, Cali, Colombia.
  • InSilico Gene Designer. Hadrien Froger, HEIG-VD, 2014. In collaboration with Recombina SA. Pamplona, Spain.
  • Producing more and better tropical crops by means of bio-inspired models. Alexandre Grillon. HEIG-VD, 2014. In collaboration with CIAT – International Center for Tropical Agriculture, Cali, Colombia.
  • Integrating Categorical Variables in Fuzzy Modelling for Biomarker Selection. Diogo Leite. HEIG-VD and ISEP (Portugal), 2014. In collaboration with SimplicityBio, Monthey, Switzerland.
  • SMPL-GRAPH: Environnement graphique pour le développement de workflows en bioinformatique. (Graphic environment for the development of bioinformatics workflows). Virgile Rumo, HES-SO, 2014. In collaboration with SimplicityBio, Monthey, Switzerland.
  • Outil de diagnostique automatisé en milieu hospitalier (Automation of a diagnostic tool in a hospital milieu). Hans Wilhelm Schwendimann, HES-SO, 2013-14. In collaboration with University Hospital of Lausanne.
  • PersoMed: Architecture d’un système de calcul de dosage de médicaments personnalisé et adaptatif (PersoMed : Architecture of a personalized and adaptive drug dose-computing system), Jean-Philippe Meylan, HES-SO, 2011
  • Librairie graphique dédiée à l’interprétation de modèles bio-inspirés (Graphic library for interpreting bio-inspired models), Pascal Berberat, HEIG-VD, 2011
  • Système bio-inspiré pour le diagnostique de cancer : analyse de stratégies de diagnostique multi-classes (Bioinspired system for cancer diagnostics : analysis of strategies for multi-class diagnostics), Direma Martea, HEIG-VD, 2011
  • Applications en bioinformatique bioinspirée : méthodes pour l’analyse et la modélisation de données métabolomiques (Applied bioinspired bioinformatics : methods for analysing and modeling metabolomics data), Alexandre Bujard, HEIG-VD, 2010
  • Système bio-inspiré pour l’analyse de données biomédicales, application au diagnostique de cancer gynécologique (Bioinspired system for biomedical data analysis, application to gynecological cancer diagnosis), Joao Dos Santos, HEIG-VD, 2009
  • Computational Systems Biology (Modelling biological networks with Probabilistic Boolean Networks), Debasree Banerjee, EPFL-Novartis, 2007
  • ODE-like fuzzy modeling of biological networks, Stephane Schnyder, EPFL-Novartis, 2007
  • Evolutionary fuzzy modeling of biological networks, Thibaut Christe, EPFL-Novartis, 2006
  • Coevolutionary fuzzy systems for solving the protein location problem, Yves Pessina, EPFL, 2004
  • Evolving finite state machines for protein location modeling, Julien Nicolas, EPFL, 2004
  • Computational model for simulating morphogenesis in living tissues, Jerôme Guerra and Marc-Antoine Nuesli, EPFL, 2004
  • A fuzzy logic-based tool for computer-assisted breast cancer diagnosis, Richard Greset, EPFL, 2001.
  • Hybrid learning (neural-evolutionary) of a fuzzy system for breast cancer diagnosis, Jean-Francois Philagor (in cooperation with M. Sipper), EPFL, 1999.